1 About the Workflow

This workflow is designed to output gene expression counts from STAR aligner using --quantmode. It will also perform general QC statistics on the fastqs with fastqc and the alignment using rseqc. Finally, the QC reports are collected into a single file using multiQC.

A DAG (directed acyclic graph) of the workflow is show below:

2 Set-up Nextflow Environment

2.1 Code Repository

First, fork the repository from Children’s bitbucket. Do this by clicking the “create fork” symbol from the bitbucket web interface and fork it to your personal bitbucket account, as illustrated below.

Next, you will need to clone your personal repository to your home in Cybertron. See the image below for where you can find the correct URL on your forked bitbucket repo.

Copy that URL to replace https://childrens-atlassian/bitbucket/scm/~jsmi26/rnaseq_count_nf.git below.

#on a terminal on the Cybertron login nodes
cd ~

# your fork should have your own userID (rather than jsmi26)
git clone https://childrens-atlassian/bitbucket/scm/~jsmi26/rnaseq_count_nf.git

cd ~/rnaseq_count_nf

Once inside the code repository, use the latest release branch or make sure you’re using the same release as prior analysis by using git.

git fetch
git branch -a

The git branch command will show all available remote branches, including remote branches, like:

* main
  remotes/origin/HEAD -> origin/main
  remotes/origin/dev
  remotes/origin/main
  remotes/origin/release/1.0.0

Checkout the most current release branch, which will be the largest value (eg use release/1.2.0 if avaiable).

You can use the most up-to-date branch by using this command:

git checkout release/1.0.0

Which will state that you are now on release/1.0.0 branch and that it is tracking the release branch in your personal repository.

Checking out files: 100% (55/55), done.
Branch release/1.0.0 set up to track remote branch release/1.0.0 from origin.
Switched to a new branch 'release/1.0.0'

2.2 Conda Environment

Finally, grab a compute node and activate the conda environment. It is also be best practice to use tmux or screen to ensure that if at the session is disconnected, then you’re nextflow workflow (if running) won’t end with SIGKILL error.

#Find your project code by listing all your projects on the Cybertron terminal
project info

#Grab a compute note
qsub -I -q freeq -l select=1:ncpus=1:mem=8g -l walltime=8:00:00 -P [PROJECT CODE]
cd /path/to/cloned/rnaseq_count_nf

If you don’t have conda installed yet, please follow these directions. You may stop following the directions after the conda deactivate step.

Next, for the conda environment to be solved, you will need to set channel_priority to flexible in your conda configs as well. To read more about conda environments and thier configurations, check out the documentation.

conda config --describe channel_priority # print your current conda settings
conda config --set channel_priority flexible # set to flexible if not already done

#Create the environement only once. Skip this step if you've already created the environment
conda env create -f env/nextflow.yaml

#Activate the conda environment. 
conda activate nextflow

3 Test the Workflow

3.1 Edit the Config File

Edit the nextflow.config file in any text editor; the example below is in R.

You will need to change the:

project code (use the same one as you used above),
the queue name to be paidq or a tier 3 queue.

Paidq will cost less than $0.01 for testing with the workflow’s example data provided in the directory test_data.

//global parameters
params {
    // general options
    sample_sheet                = "test_data/paired_end_sample_sheet.csv"
    download_sra_fqs            = false
    queue                       = 'paidq'
    project                     = '[PROJECT CODE]'
<...continues...>

usethis::edit_file("../nextflow.config")

3.2 Paired-end example

Determine if the workflow works on your installation of the conda environment by running the following command.

./main_run.sh "paired_end_test"

3.3 Single-end example

To test the single-end sheet, modify the sample_sheet parameter in the nextflow.config and the output directory (outdir).

params {
    // general options
    sample_sheet                = "test_data/single_end_sample_sheet.csv"
    [...]
    outdir                      = "./single_end_results/"
<...continues...>
}

then run the command

./main_run.sh "single_end_test"

3.4 sra download example

To test the sra sample sheet - modify these the sample sheet, and set download_sra_fastqs to true in the nextflow.config and the output directory (outdir).

params {
    // general options
    sample_sheet                = "test_data/sra_sample_sheet.csv"
    download_sra_fqs            = true
    [...]
    outdir                      = "./sra_results/"
<...continues...>
}

then run on the command:

./main_run.sh "sra_test"

4 Modify the Pipeline for Your Data

4.1 Define Input Files

A comma delimited (csv) sample sheet is required for the input samples to be processed. Please note, do not remove the comment lines that begin with “#” in the example files. The same number of comment lines must be included in any input sample sheet, based on the examples provided here.

It must have the column names (in any order):

r1 - the filepath for the read 1 fastq in paired-end RNA-seq, or the single-end fastq file
r2 - the filepath for the read 2 fastq in paired-end RNA-seq
id - unique sample ID, no duplicates allowed in the sample sheet
single_end - boolean [true/false] if the data is single-end or paired-end

TO DO: A function provided here, but it may not meet the needs of every experiment.

The two examples are provided here to look at:

example_sheet <- read.csv(here::here("test_data/paired_end_sample_sheet.csv"),
    header = TRUE, comment.char = "#")
example_sheet

If downloading the fastq files directly from the SRA, the sample sheet only requires the id and the single_end columns.

sra_example <- read.csv(here::here("test_data/sra_sample_sheet.csv"),
    header = TRUE, comment.char = "#")

sra_example

4.2 Nextflow Config

Edit the nextflow.config file to include the appropriate filepaths for the samples to be included in the pipeline, and the appropriate genome references. The required files are listed here:

## //working directory for temporary/intermediate files produced in the workflow processes
## workDir = "$HOME/temp"
## 
## //global parameters
## params {
##     // general options
##     sample_sheet                = "test_data/sra_sample_sheet.csv"
##     queue                       = 'paidq'
##     project                     = '207f23bf-acb6-4835-8bfe-142436acb58c'
## 
##     // Input and output options
##     download_sra_fqs            = true
##     outdir                      = "./sra_results/"
##     publish_dir_mode            = 'copy'
## 
##     // STAR specific params
##     index                       = '/gpfs/shared_data/STAR/human_GRCh38_ensembl_v106/star'
##     build_index                 = false
##     gtf                         = '/gpfs/shared_data/STAR/human_GRCh38_ensembl_v106/Homo_sapiens.GRCh38.106.gtf' // required
##     fasta                       = '/gpfs/shared_data/STAR/human_GRCh38_ensembl_v106/Homo_sapiens.GRCh38.dna.primary_assembly.fa' // required
## <...>

4.3 Genome References

Rseqc reference bed files are generated using the provided GTF file in params section of the nextflow.config file. The ref_gene_model is generated in BED12 format using UCSC utilities (Kent tools). This allows the Rseqc references to match the transcript IDs used in the GTF for STAR aligner. The pipeline does require the user to provide a list of rRNA transcript IDs that match the transcript ID format in the provided GTF.

The easiest way to find the rRNA transcripts is to use Ensembl Biomart or UCSC table browser. For Ensembl, use a filter based on the rRNA biotypes you would like to quantify (eg Mt_rRNA , rRNA , rRNA_pseudogene) and then select “Transcript stable ID” as the Attribute to save to file.

In R, this could be accomplished with:

library(biomaRt)
species <- "Homo_sapiens"
mart <- useEnsembl('ensembl', dataset = 'Homo_sapiens_gene_ensembl')
biomaRt::getBM(values=c("rRNA", "rRNA_pseudogene","Mt_rRNA"),
               filters="biotype", 
               attributes=c("ensembl_transcript_id"), 
               mart = mart)

An example of the gene models and rRNA bed files can be found at the RSEQC documentation page and are located at /gpfs/shared_data/rseqc to share with the SCRI.

4.4 Advanced Options

In the nextflow.config, you can define additional command line arguments to the scientific software under process scope.

## // Computational resource allocation for the processes run in the workflow
## process {
##     publishDir = [
##         path: { "${params.outdir}/${task.process.tokenize(':')[-1].tokenize('_')[0].toLowerCase()}" },
##         mode: params.publish_dir_mode,
##         saveAs: { filename -> filename.equals('versions.yml') ? null : filename }
##     ]
##     errorStrategy = "retry"
##     maxRetries = 2
## 
##     //STAR-aligner process specific parameters
##     //https://www.nextflow.io/docs/latest/process.html#dynamic-computing-resources
##     withName: STAR_ALIGN {
##         cpus = { 4 * task.attempt }
##         memory = { 32.GB * task.attempt }
## <...>

You may use the advanced options to change computational resources requested for different processes. The CPUs and memory parameters can updated to request a larger amount of resources like CPUs or memory if files are large. You may also edit the commandline parameters for processes in the workflow using the ext.args directive.

The current TRIMGALORE paramters look like this:

##     //Trimgalore process specific parameters
##     withName: TRIMGALORE {
##         cpus = { 2 * task.attempt }
##         memory = { 8.GB * task.attempt }
##         ext.args = ''
##     }
## 
## <...>

But you’d like to request 16Gb of memory for and gzip the output.

##     //Trimgalore process specific parameters
##     withName: TRIMGALORE {
##         cpus = { 2 * task.attempt }
##         memory = { 16.GB * task.attempt }
##         ext.args = '--gzip'
##     }
## 
## <...>

5 Run the workflow

Then execute a wrapper around the nextflow run main.nf command which is in the main_run.sh shell script. Provide a descriptive name (string) for your workflow run, in this example we will use “my_analysis”.

Typically, you will not need to change the main_run.sh often.

The main_run.sh script defines the profiles for different executors in the variable NFX_PROFILE. The choices for profiles are:

PBS_singularity [default]
local_singularity
PBS_conda

“PBS_singularity” is recommended. This profiles executes the jobs on the HPC using the PBS scheduler and then will run the job inside singularity containers with the appropriate scientific software versions.

“local_singularity” is good for workflow development if you’re making a lot of changes. This will use singularity on Cybertron, but run the jobs on the interactive compute node that you’ve requested during “Set-up Nextflow Environment” steps above.

“PBS_conda” is generally not recommended.This profiles executes the jobs on the HPC using the PBS scheduler. Then nextflow will coordinate the creation of conda environments for each step in the workflow and active those environments in the scheduled compute nodes for job.

## #!/bin/bash
## 
## set -eu
## DATE=$(date +%F)
## NFX_CONFIG=./nextflow.config
## #Options: PBS_apptainer, local_apptainer, PBS_singularity,local_singularity
## NFX_PROFILE='PBS_apptainer'
## #Options:  rnaseq_count, prep_genome, or sra_download
## NFX_ENTRY='sra_download'
## #The output prefix on filenames for reports/logs
## <...>

You can also change the entry_point for the workflow.

Run only the index building step using NFX_ENTRY='star_index'.
Run only the download step for SRA files by setting NFX_ENTRY='sra_fastqs'.
Keep the default NFX_ENTRY='rnaseq_count' to run the complete pipeline.

./main_run.sh "my_analysis"

6 Expected Outputs

Under the path provided in the nextflow config for params “outdir”, you will find directories named for each of the modules. Lets say “params.outdir = ./results”. There will be the following file structure:

results/

fastqc/
- fastqc_{sample_id}_/
multiqc/
- {sample_sheet_basename}_multiqc_report_data/
- collects fastqc, star alignment, and star quantification stats
picard/
- {sample_id}.bam
- {sample_id}.MarkDuplicates.metrics.txt
rseqc/
- {gft_basename}.sort.rRNA.bed
- {sample_id}.rRNA_stats.out
- {sample_id}.in.bam - rRNA reads
- {sample_id}.Aligned.sortedByCoord.out.summary.txt
- {sample_id}.Aligned.sortedByCoord.out.tin.xls
- {sample_id}.read_distribution.txt
samtools/
- fasta index (.fai) file
- bam index (.bai) file
sratools/
- {SRR_RUN_ID}.fastq.gz
- sratoolkit config file
star/
- star/[SA, SAindex, etc] - optional star index files if build_index = true
- {sample_id}.Aligned.out.bam
- {sample_id}.Log.final.out
- {sample_id}.Log.out
- {sample_id}.Log.progress.out
- {sample_id}.ReadsPerGene.out.tab
- {sample_id}.SJ.out.tab
ucsc/
- {gft_basename}.genepred
- {gft_basename}.sort.bed
- {gft_basename}.refflat
- {gft_basename}_transcript.infoOut.txt

In addition, there will be an HTML report with information on where the temp data is stored in the workDir path, and general run statistics such as resource utilized versus requested, which helps with optimization. It will also provide information on how much walltime was used per sample, total CPU hours, etc.

The HTML file is found in reports directory and will have the prefix defined on the command line when the ./main_run.sh "my_analysis" was invoked, so in this example it would be named “my_analysis_{DATE}.html”.

There will also be a detailed nextflow log file that is useful for de-bugging which will also be named in this example, “my_analysis_{DATE}_nextflow.log”.

Finally, the pipeline will produce a DAG - Directed acyclic graph which describes the workflow channels (inputs) and the modules. The DAG image will be saved under dag/ directory with the name “my_analysis_{DATE}_dag.pdf”.

8 Cleaning up Cached Data

Nextflow has an utility to clean up old work directories and logs that are no longer needed. This can be run after any amount of time to keep your workdir from getting too large or if you’re running out of disk space.

This requires the session ID or session name, which can found in the .nextflow/history file.

nextflow log
nextflow clean -f [RUN NAME]

9 Session Information

sessionInfo()

Nextflow ‘rnaseq_count_nf’ Pipeline Run Instructions

Jenny L Smith

2023-09-26

1 About the Workflow

2 Set-up Nextflow Environment

2.1 Code Repository

2.2 Conda Environment

3 Test the Workflow

3.1 Edit the Config File

3.2 Paired-end example

3.3 Single-end example

3.4 sra download example

4 Modify the Pipeline for Your Data

4.1 Define Input Files

4.2 Nextflow Config

4.3 Genome References

4.4 Advanced Options

5 Run the workflow

6 Expected Outputs

8 Cleaning up Cached Data

9 Session Information

Nextflow ‘rnaseq_count_nf’ Pipeline Run Instructions

Jenny L Smith

2023-09-26

1 About the Workflow

2 Set-up Nextflow Environment

2.1 Code Repository

2.2 Conda Environment

3 Test the Workflow

3.1 Edit the Config File

3.2 Paired-end example

3.3 Single-end example

3.4 sra download example

4 Modify the Pipeline for Your Data

4.1 Define Input Files

4.2 Nextflow Config

4.3 Genome References

4.4 Advanced Options

5 Run the workflow

6 Expected Outputs

7 Share the Data

8 Cleaning up Cached Data

9 Session Information